December 14, 2018
Putting Digital Health to Work
Consumers increasingly value experiences above physical things. Can a new breed of protection products push them toward better health?
The U.K. spends £97 billion treating diseases but just £8 billion preventing them. This imbalance is set to change according to government proposals. Under a social prevention model, health advice would be tailored to an individual based on several criteria, including personal data, lifestyle and demographics.
There are parallels to insurance. The Association of British Insurers has reported that U.K. life insurers paid out £5 billion in income protection, critical illness and life assurance claims in 2017. These claims payments represent amounts paid when people’s health has failed. While not every diagnosis or early death can be avoided, providers could offer more to help customers mitigate risk and stay healthy.
This predicament is fueling interest in matching insurance programs to fitness data. There are multiple digital-based solutions available to help with engagement post-underwriting — a white space for insurers to move into. Gen Re is active in researching technology of this type, which has led us to collaborations with a network of established companies and startups in an effort to create a prevention model.
One such company is PAI Health. It offers a proprietary, science-based algorithm that uses cardiorespiratory fitness (CRF) to provide personalized guidance on how much exercise is needed for optimal health.
See also: New Health Metrics in Life Insurance
In a 2018 article, Mandsager et al. confirmed CRF is a modifiable risk indicator of long-term mortality that is quite independent of age, sex and comorbidities. CRF is also associated with cardiovascular and other health benefits, including reductions in coronary artery disease, hypertension, diabetes, stroke and even cancer. CRF is inversely associated with long-term mortality with no observed upper limit of benefit. Extremely high aerobic fitness was associated with the greatest survival.
That said, taking the right dose of physical exercise is very important. Too much exercise means a risk of adverse outcomes leading to the idea of a U-shaped dose-response association between exercise and cardiovascular events. PAI Health works by linking the individual to the dose.
This personalized approach is critically important to ensure an insurance program is built around physical activity that appeals to the broadest range of people, and not just those who live in Lycra. In other words, an insurance program that provides benefit to everyman based on achievable yet therapeutic levels of everyday physical activity.
It can be challenging to untangle large amounts of data and turn it into meaningful health insights. It’s important that there is evidence to validate the algorithms and “health scores” promoted in apps. All exercise is beneficial to health, but it’s well-known that steps lack scientific reasoning. A daily target of 10,000 steps is daunting, even unrealistic, and lacks any calibration to the individual and to physical capability.
PAI Health avoids these problems. The Physical Activity Intelligence (PAI) algorithm was invented by Ulrik Wisløff, head of the Cardiac Exercise Research Group and professor at the Norwegian University of Science and Technology. External evidence supports the conclusion that meeting a personal PAI target cuts cardiovascular risk, significantly reduces other lifestyle-related diseases in men and women of all ages and increases life expectancy. This has been shown to also be true in patients with established cardiovascular disease.
Preventative medicine is about ensuring people take greater responsibility for their health and well-being. Most insurers could do more to engage policyholders in this way. Research suggests consumers increasingly value experiences above physical things. Can a new breed of protection products offer people more of an experience? A policy that actively involves them in protecting their own health could offer that.
For any national health service to link care to personal data requires the highest standards of data privacy, and insurance is no different. While a prevention approach to healthcare is unlikely to be without controversy, the major barrier to a social prevention model is diverting funds away from treatment. For insurers, the problems may be less knotty. Elegant solutions like PAI Health are ready to be utilized.